David M. Kroenke and David J. Auer Database Processing: Fundamentals, Design and Implementation

Transcription

1 David M. Kroenke and David J. Auer Database Processing: Fundamentals, Design and Implementation Chapter Two: Introduction to Structured Query Language 2-1

2 Chapter Objectives To understand the use of extracted data sets. To understand the use of ad-hoc queries. To understand the history and significance of Structured Query Language (SQL). To understand the SQL SELECT/FROM/WHERE framework as the basis for database queries. To be able to write queries in SQL to retrieve data from a single table. To be able to write queries in SQL to use the SQL SELECT, FROM, WHERE, ORDER BY, GROUP BY, and HAVING clauses. 2-2

3 Chapter Objectives To be able to write queries in SQL to use SQL DISTINCT, AND, OR, NOT, BETWEEN, LIKE, and IN keywords. To be able to use the SQL built-in functions of SUM, COUNT, MIN, MAX, and AVG with and without the use of a GROUP BY clause. To be able to write queries in SQL to retrieve data from a single table but restricting the data based upon data in another table (subquery). To be able to write queries in SQL to retrieve data from multiple tables using an SQL join. 2-3

4 Structured Query Language Structured Query Language (SQL) was developed by the IBM Corporation in the late 1970 s. SQL was endorsed as a United States national standard by the American National Standards Institute (ANSI) in 1992 [SQL-92]. Newer versions exist, and incorporate XML and some object-oriented concepts. 2-4

5 SQL as a Data Sublanguage SQL is not a full featured programming language. C, C#, Java SQL is a data sublanguage for creating and processing database data and metadata. SQL is ubiquitous in enterprise-class DBMS products. SQL programming is a critical skill. 2-5

6 SQL DDL and DML SQL statements can be divided into two categories: Data definition language (DDL) statements Used for creating tables, relationships and other structures. Covered in Chapter Seven. Data manipulation language (DML) statements. Used for queries and data modification. Covered in this chapter (Chapter Two). 2-6

7 Cape Codd Outdoor Sports Cape Codd Outdoor Sports is a fictitious company based on an actual outdoor retail equipment vendor. Cape Codd Outdoor Sports: Has 15 retail stores in the US and Canada. Has a on-line Internet store. Has a (postal) mail order department. All retail sales recorded in an Oracle database. 2-7

8 Cape Codd Retail Sales Structure 2-8

9 Cape Codd Retail Sales Data Extraction The Cape Codd marketing department needs an analysis of in-store sales. The entire database is not needed for this, only an extraction of retail sales data. The data is extracted by the IS department from the operational database into a separate, off-line database for use by the marketing department. Three tables are used: RETAIL_ORDER, ORDER_ITEM, and SKU_DATA (SKU = Stock Keeping Unit). The extracted data is converted as necessary: Into a different DBMS MS SQL Server Into different columns OrderDate becomes OrderMonth and OrderYear. 2-9

10 Extracted Retail Sales Data Format 2-10

11 Retail Sales Extract Tables [in MS SQL Server] 2-11

12 The SQL SELECT Statement The fundamental framework for SQL query states is the SQL SELECT statement. SELECT {ColumnName(s)} FROM {TableName(s)} WHERE {Conditions} All SQL statements end with a semi-colon (;). 2-12

13 Specific Columns on One Table SELECT FROM Department, Buyer SKU_DATA; 2-13

14 Specifying Column Order SELECT FROM Buyer, Department SKU_DATA; 2-14

15 The DISTINCT Keyword SELECT FROM SKU_DATA; DISTINCT Buyer, Department 2-15

16 Selecting All Columns: The Asterisk (*) Keyword SELECT * FROM SKU_DATA; 2-16

17 Specific Rows from One Table SELECT * FROM SKU_DATA WHERE Department = 'Water Sports'; NOTE: SQL wants a plain ASCII single quote: ' NOT! 2-17

18 Specific Columns and Rows from One Table SELECT FROM WHERE SKU_Description, Buyer SKU_DATA Department = 'Climbing'; 2-18

19 Using MS Access 2-19

20 Using MS Access (Continued) 2-20

21 Using MS Access (Continued) 2-21

22 Using MS Access (Continued) 2-22

23 Using MS Access (Continued) 2-23

24 Using MS Access - Results 2-24

25 Using MS Access Saving the Query 2-25

26 Using MS SQL Server 2008 The Microsoft SQL Server Management Studio I 2-26

27 Using MS SQL Server 2008 The Microsoft SQL Server Management Studio II 2-27

28 Using Oracle Database 11g SQL Developer I 2-28

29 Using Oracle Database 11g SQL Developer II 2-29

30 Using MySQL 5.1 MySQL Query Browser I 2-30

31 Using MySQL 5.1 MySQL Query Browser II 2-31

32 Sorting the Results ORDER BY SELECT * FROM ORDER BY ORDER_ITEM OrderNumber, Price; 2-32

33 Sort Order: Ascending and Descending SELECT * FROM ORDER_ITEM ORDER BY Price DESC, OrderNumber ASC; NOTE: The default sort order is ASC does not have to be specified. 2-33

34 WHERE Clause Options - AND SELECT * FROM SKU_DATA WHERE Department = 'Water Sports' AND Buyer = 'Nancy Meyers'; 2-34

35 WHERE Clause Options - OR SELECT * FROM SKU_DATA WHERE Department = 'Camping' OR Department = 'Climbing'; 2-35

36 WHERE Clause Options - IN SELECT * FROM SKU_DATA WHERE Buyer IN ('Nancy Meyers', 'Cindy Lo', 'Jerry Martin'); 2-36

37 WHERE Clause Options NOT IN SELECT * FROM SKU_DATA WHERE Buyer NOT IN ('Nancy Meyers', 'Cindy Lo', 'Jerry Martin'); 2-37

38 WHERE Clause Options Ranges with BETWEEN SELECT * FROM ORDER_ITEM WHERE ExtendedPrice BETWEEN 100 AND 200; 2-38

39 WHERE Clause Options Ranges with Math Symbols SELECT * FROM ORDER_ITEM WHERE ExtendedPrice >= 100 AND ExtendedPrice <= 200; 2-39

40 WHERE Clause Options LIKE and Wildcards The SQL keyword LIKE can be combined with wildcard symbols: SQL 92 Standard (SQL Server, Oracle, etc.): _ = Exactly one character % = Any set of zero or more characters MS Access (based on MS DOS)? * = Exactly one character = Any set of zero or more characters 2-40

41 WHERE Clause Options LIKE and Wildcards SELECT * FROM SKU_DATA WHERE Buyer LIKE 'Pete%'; 2-41

42 WHERE Clause Options LIKE and Wildcards SELECT * FROM SKU_DATA WHERE SKU_Descripton LIKE '%Tent%'; 2-42

43 WHERE Clause Options LIKE and Wildcards SELECT * FROM SKU_DATA WHERE SKU LIKE '%2 '; 2-43

44 SQL Built-in Functions There are five SQL Built-in Functions: COUNT SUM AVG MIN MAX 2-44

45 SQL Built-in Functions SELECT SUM (ExtendedPrice) AS Order3000Sum FROM ORDER_ITEM WHERE OrderNumber = 3000; 2-45

46 SQL Built-in Functions SELECT FROM SUM (ExtendedPrice) AS OrderItemSum, AVG (ExtendedPrice) AS OrderItemAvg, MIN (ExtendedPrice) AS OrderItemMin, MAX (ExtendedPrice) AS OrderItemMax ORDER_ITEM; 2-46

47 SQL Built-in Functions SELECT FROM COUNT(*) AS NumberOfRows ORDER_ITEM; 2-47

48 SQL Built-in Functions SELECT FROM COUNT (DISTINCT Department) AS DeptCount SKU_DATA; 2-48

49 Arithmetic in SELECT Statements SELECT FROM Quantity * Price AS EP, ExtendedPrice ORDER_ITEM; 2-49

50 String Functions in SELECT Statements SELECT FROM DISTINCT RTRIM (Buyer) + ' in ' + RTRIM (Department) AS Sponsor SKU_DATA; 2-50

51 The SQL keyword GROUP BY SELECT Department, Buyer, COUNT(*) AS Dept_Buyer_SKU_Count FROM SKU_DATA GROUP BY Department, Buyer; 2-51

52 The SQL keyword GROUP BY In general, place WHERE before GROUP BY. Some DBMS products do not require that placement, but to be safe, always put WHERE before GROUP BY. The HAVING operator restricts the groups that are presented in the result. There is an ambiguity in statements that include both WHERE and HAVING clauses. The results can vary, so to eliminate this ambiguity SQL always applies WHERE before HAVING. 2-52

53 The SQL keyword GROUP BY SELECT Department, COUNT(*) AS Dept_SKU_Count FROM SKU_DATA WHERE SKU <> GROUP BY Department ORDER BY Dept_SKU_Count; 2-53

54 The SQL keyword GROUP BY SELECT Department, COUNT(*) AS Dept_SKU_Count FROM SKU_DATA WHERE SKU <> GROUP BY Department HAVING COUNT (*) > 1 ORDER BY Dept_SKU_Count; 2-54

55 Querying Multiple Tables: Subqueries SELECT FROM WHERE SUM (ExtendedPrice) AS Revenue ORDER_ITEM SKU IN (SELECT SKU FROM SKU_DATA WHERE Department = 'Water Sports'); Note: The second SELECT statement is a subquery. 2-55

56 SELECT FROM WHERE Querying Multiple Tables: Buyer SKU_DATA SKU IN (SELECT FROM WHERE Subqueries SKU ORDER_ITEM OrderNumber IN (SELECT OrderNumber FROM RETAIL_ORDER WHERE OrderMonth = 'January' AND OrderYear = 2004)); 2-56

57 Querying Multiple Tables: Joins SELECT FROM WHERE Buyer, ExtendedPrice SKU_DATA, ORDER_ITEM SKU_DATA.SKU = ORDER_ITEM.SKU; 2-57

58 Querying Multiple Tables: Joins SELECT FROM WHERE GROUP BY ORDER BY Buyer, SUM(ExtendedPrice) AS BuyerRevenue SKU_DATA, ORDER_ITEM SKU_DATA.SKU = ORDER_ITEM.SKU Buyer BuyerRevenue DESC; 2-58

59 Querying Multiple Tables: Joins SELECT Buyer, ExtendedPrice, OrderMonth FROM SKU_DATA, ORDER_ITEM, RETAIL_ORDER WHERE SKU_DATA.SKU = ORDER_ITEM.SKU AND ORDER_ITEM.OrderNumber = RETAIL_ORDER.OrderNumber; 2-59

60 Subqueries versus Joins Subqueries and joins both process multiple tables A subquery can only be used to retrieve data from the top table. A join can be used to obtain data from any number of tables, including the top table of the subquery. In Chapter 7, we will study the correlated subquery. That kind of subquery can do work that is not possible with joins. 2-60

61 David Kroenke and David Auer Database Processing Fundamentals, Design, and Implementation (11 th Edition) End of Presentation: Chapter Two 2-61